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Budgeted data caching based on k-Median in mobile edge computing

Version 2 2024-06-04, 15:05
Version 1 2021-01-21, 15:32
conference contribution
posted on 2024-06-04, 15:05 authored by X Xia, Feifei ChenFeifei Chen, G Cui, Mohamed AbdelrazekMohamed Abdelrazek, J Grundy, H Jin, Q He
© 2020 IEEE. In mobile edge computing (MEC), edge servers are deployed at base stations to provide highly accessible computational resources and storage capacities to nearby mobile devices. Caching data on edge servers can ensure the service quality and network latency for those mobile devices. However, an app vendor needs to ensure that the data caching cost does not exceed its data caching budget. In this paper, we present the budgeted edge data caching (BEDC) problem as a constrained optimization problem to maximize the overall reduction in data retrieval for all its app users within the budget, and prove that it is NP-hard. Then, we provide an approach named IP-BEDC for solving the BEDC problem optimally based on Integer Programming. We also provide an O(k) -approximation algorithm, namely α-BEDC, to find near-optimal solutions to the BEDC problems efficiently. Our proposed approaches are evaluated on a real-world data set and a synthesized data set. The results demonstrate that our approaches can solve the BEDC problem effectively and efficiently while significantly outperforming five representative approaches.

History

Pagination

197-206

Location

Online from Beijing, China

Start date

2020-10-19

End date

2020-10-23

ISBN-13

9781728187860

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICWS 2020 : Proceedings of the 2020 IEEE 13th International Conference on Web Services

Event

IEEE Computer Society. International Conference (13th : 2020 : Online from Beijing, China)

Publisher

Institute of Electrical and Electronics Engineers

Place of publication

Piscataway, N.J.

Series

IEEE Computer Society International Conference

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